Classification Trees
The goal of a classification tree is to sequentially partition the data to maximize the
differences in the dependent variable. It is often referred to as a decision tree. The true
purpose of a classification tree is to classify the data into distinct groups or branches
that create the strongest separation in the values of the dependent variable.
Classification trees are very good at identifying segments with a desired behavior such
as response or activation. This identification can be quite useful when a company is
trying to understand what is driving market behavior. It also has an advantage over
regression in its ability to detect nonlinear relationships. Classification trees are “grown”
through a series of steps and rules that offer great flexibility. In Figure, the tree
differentiates between responders and non responders. The top node represents the
performance of the overall campaign. Sales pieces were mailed to 10,000 names and
yielded a response rate of 2.6%. The first split is on gender. This implies that the
greatest difference between responders and non responders is gender. We see that
males are much more responsive (3.2%) than females (2.1%).

If we stop after one split, we would consider males the better target group. Our goal,
though, is to find groups within both genders that discriminate between responders and
non responders. In the next split, these two groups or nodes are considered separately.
The second-level split from the male node is on income. This implies that income level
varies the most between responders and non responders among the males. For females,
the greatest difference is among age groups. It is very easy to identify the groups with
the highest response rates. Let’s say that management decides to mail only to groups
where the response rate is more than 3.5%. The offers would be directed to males who
make more than $30,000 a year and females over age 40. A definite advantage of
classification trees over other techniques is their ability to explain the results. We can
often develop complex logistic models for scoring and build a tree to explain the results
to the marketers. Although the outcome is never identical, the tree does a good job of
uncovering key drivers in the market. Due to their broad applicability, classification trees
will continue to be a valuable tool for all types of target modeling.
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